Supply Chain 2012 Part 4 pptx

Contracts such as buy-back contract, revenue sharing contract, quantity flexibility contract and rebates contract are all known forms of contract which can help to achieve channel coordi

Relative contractual

power

Propensity to

Distributor Retailer Distributor Retailer

High High Low Low 0.366697 58.90% 2353.584 786.064 1567.52 High High High Low 0.368474 66.00% 2384.233 813.5713 1570.662 High High Low High 0.365149 66.30% 2385.529 809.0421 1576.486 High High High High 0.36616 73.00% 2414.452 833.8039 1580.648 Low High Low Low 0.359251 60.10% 2358.764 778.885 1579.879 Low High High Low 0.360741 64.10% 2376.031 794.3732 1581.658 Low High Low High 0.362365 59.40% 2355.742 781.2793 1574.463 Low High High High 0.354521 69.60% 2399.774 801.5961 1598.178 High Low Low Low 0.37171 61.10% 2363.081 801.4199 1561.661 High Low High Low 0.37279 58.10% 2350.13 792.2589 1557.871 High Low Low High 0.37223 58.80% 2353.152 793.9527 1559.199 High Low High High 0.37048 67.90% 2392.436 823.6709 1568.765 Low Low Low Low 0.37006 55.40% 2338.474 778.6829 1559.792 Low Low High Low 0.37154 53.80% 2331.567 775.0321 1556.535 Low Low Low High 0.36048 62.00% 2366.966 787.0223 1579.944 Low Low High High 0.37016 58.10% 2350.13 788.3914 1561.739 Table 3 Results

Contractual Power Propensity to

Table 4 SC profits

Also, the results are quite poor when the propensity to collaborate is low for both agents

(fourth row), regardless the contractual power In this case the agents are not able to reach

an agreement on the value of), given that they tend to modify their initial preference at a

lower rate (low '))

Leaving out these worst cases (last column and row), the best SC profits are achieved when both agents are highly propense to collaborate (first row) In fact, in this case the agreement

is reached with a higher frequency, given that both the agents modify the ) value with a higher pace (higher ')) Only when both agents have low contractual power (last column),

a high propensity to collaborate of both is not enough to guarantee an adequate percentage

of agreement: this could depends on the sensible reduction of those agreements wherein the negotiation ends because one of the parties forces the other to accept his bid This seems to

be confirmed by the good results achieved when both agents have high contractual power

(first column): the high quota of “forced” agreements compensate the possible lower propensity to collaborate

Notice that the best scenario, characterized by high contractual power and propensity to collaborate for both agents, is associated with the highest number of agreements (73%)

82In this case even though the value of the average ) is not the highest (which would let think the retailer to miss his highest possible profit), the retailer gains the highest profit, due

to a higher number of agreements Furthermore, also the distributor achieves a good performance (i.e the best third one of its results)

5 Conclusions

The revenue sharing contract is a coordination mechanism adopted by supply chains, wherein the decision making process is decentralized, to assure channel coordination It has been mainly used in the video-rental industry by firms such as Blockbuster or Hollywood Planet Despite the ease of this coordination mechanism, based on two parameters, the RS contract is not much widespread in other industries due to implementation problems We have then analyzed this issue

First, we have defined the features of the video rental industry which we believe critical with respect to the RS contract adoption This has allowed other industries to be identified

as potential users of the contract Then, we have described the design of a RS contract for a two-stage SC that assures the channel coordination and allows the SC actors to increase their profits

Successively, we have developed an agent-based system model of the negotiation process between the two SC actors which takes into account two further variables, which we believe

to play a key role for the negotiation: the relative contractual power and the collaboration of the SC actors

In the proposed model, the two agents (i.e the SC actors) negotiate on the value of the contract parameter that influences the SC profit sharing between them Based on the agent beliefs influencing their behaviors, the negotiation process can end in different ways: either the agents reach an agreement on the value of the parameter, or they can not reach such an agreement (which results in the SC not adopting the contract and operating under a market setting)

Finally, we have carried out a simulation analysis aimed at identifying the scenarios in which the RS is more likely to be adopted In particular, we have measured how many times the negotiation ends with an agreement and the agreed value of the parameter

The simulation has shown that high propensity to collaborate for both SC actors and high contractual power of al least one SC actor prove critical for the RS implementation In this case only the collaboration of retailer can increase the SC profit Further research will be devoted to extend the model to different SC topologies (e.g SCs made up of one distributor and multiple retailers)

6 References

Albino V., Carbonara N and I Giannoccaro, 2007, Supply Chain Cooperation within

Industrial Districts: A Simulation Analysis, European Journal of Operational Research, Vol 177 No 1, 261-280

Bensaou, M.,1999, Portfolios of buyer-supplier relationship, Sloan Management Review 2,

35-44

Cachon, G., Lariviere, M.A., 2005, Supply Chain Coordination with Revenue Sharing

Contracts: Strengths and Limitations, Management Science 51, 30-44

Cachon G., 2004, Supply Chain Coordination with Contracts, in Supply Chain management:

Design, Coordination, and Operations, A.G de Kok and S.C Graves (Eds.), North Holland

Cantamessa, M., 1997, Agent-based modelling and management manufacturing systems,

Computers in Industry 34, 173-186

Durfee, E., 1988, Coordination of distributed problem solvers, Kluwer Academic Publishers,

Boston

Emmons, H., Gilbert, S.M., 1998, Note: the Role of Returns policies in Pricing and Inventory

decisions for Catalogue Goods, Management Science, Vol 44, No 2

Eppen, G.D., Iyer, A.V., 1997, Backup agreements in Fashion Buying – the Value of

upstream Flexibility, Management Science, Vol 43, No 11

Federgruen, A., 1993, Centralized planning models for multi-echelon inventory systems

under uncertainty, in: Graves et al (Eds.) Handbooks in OR & MS, Logistics of Production and Inventory, Vol 4, North Holland, Amsterdam, pp.133-173

Ferber, J., 1999, Multi-Agent Systems An Introduction to Distributed Artificial Intelligence,

Addison-Wesley, London

Giannoccaro I., Pontrandolfo, P., 2004, Supply Chain Coordination by Revenue sharing

contracts, International Journal of Production Economics, forthcoming

Grant, R M., 1991, Contemporary Strategy Analysis Concepts, Techniques, Applications,

(Blackwell, Oxford)

Lee, H., Whang S., 1999, Decentralized Multi-echelon Supply chains: Incentives and

Information, Management Science, Vol 45, No 5

Lin, F R and M J Shaw, 1998, Re-engineering the order fulfilment process in supply chain

network, International Journal Flexible Manufacturing Systems 10, 197-229

Swaminathan, J M., Smith, S F and N M Sadeh, 1998, Modeling Supply Chain Dynamics:

A Multi-agent Approach, Decision Sciences 29, 607-632

Tsay, A., 1999, The Quantity Flexibility Contract and Supplier-Customer Incentives,

Management Science, Vol 45, No 10

Tsay, A., Nahmias, S., Agrawal, N., Modeling Supply Chain Contracts: a Review, Chapter

10, Quantitative Models for Supply Chain Management, Tayur S., Ganeshan R.,

Magazine M (Eds), Kluwer Academic Publishers, 1999

Weng, Z.K., 1995 Channel Coordination and Quantity Discounts, Management Science, Vol

41, No 9

Whang, S., 1995, Coordination in Operations: a Taxonomy, Journal of Operations Management,

12, 413-422

Wooldridge, M., 2000, Intelligent Agents, in Weiss G (ed.), Multi-agent Systems A modern

approach to distributed artificial intelligence, The MIT Press, Cambridge (Massachusetts)

Mean-Variance Analysis of Supply Chain

“supply chain management encompasses the planning and management of all activities involved in

sourcing and procurement, conversion, and all logistics management activities Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers In essence, supply chain management integrates supply and demand management within and across companies.” From this description,

it is obviously true that a supply chain in general has multiple channel members (usually called stages) and the coordination and collaboration among these members is a crucial task

in supply chain management

In the literature, various policies for supply chain optimization and channel coordination have been proposed Among them, setting a supply chain contract between individual parties has received much attention in recent years (Tsay et al 1999, Cachon 2003) Contracts such as buy-back contract, revenue sharing contract, quantity flexibility contract and rebates contract are all known forms of contract which can help to achieve channel coordination in a supply chain However, in the majority of the literature works, the channels' and supply chain’s objectives are either maximizing the expected profit or minimizing the expected cost There is no discussion on the level of risk associated with these contracts As a result, the contract parameters under which coordination is achieved may be viewed as unrealistic by decision makers In light of this, we conduct in this paper a mean-variance analysis on some popular forms of supply chain contracts such as buy-back contract By including a constraint

on profit uncertainty, we illustrate how decision makers can make a scientifically sound and tailored decision with respect to their degrees of risk aversion Managerial implications are discussed

The organization of the rest of this chapter is as follows: We briefly review some related literature in Section 2, the discussion of the supply chain’s structure is presented in Section

3 The mean-variance analyses on the buy-back contract and wholesale pricing profit sharing contract are conducted in Sections 4 and 5, respectively We conclude with some discussions on managerial implications in Section 6

For a notational purpose, we use the following notation in many places throughout this

chapter: P = profit, EP = expected profit, SP = standard deviation of profit, MV =

mean-variance The subscripts “M, R, SC” represent “Manufacturer, Retailer, Supply Chain”,

respectively

2 Literature review

Pioneered by Nobel laureate Harry Markowitz in the 1950s, the mean-variance formulation has become a fundamental theory for risk management in finance (Markowitz 1959) In decision sciences, the mean-variance approach and the von Neumann-Morgenstern utility approach (called utility function approach in short) are two well established methodologies for studying decision making problems with risk concerns The utility function approach is more precise but its application is limited owing to the difficulty in getting a closed form expression of the utility function for every individual decision maker in practice The mean-variance approach, as what Van Mieghem (2003) mentioned, aims at providing an implementable, useful but approximate solution It is true that a utility function in general cannot be expressed fully in terms of mean and variance only However, it is shown in Van Mieghem (2003) that maximizing a utility function with a constant coefficient of risk aversion is equivalent to maximizing a mean-variance performance measure (also see Luenberger 1998, Choi et al 2008 for some supplementary discussions) There are also evidences in the literature which demonstrate that the mean-variance approach yields a solution which is close to the optimal solution under the utility function approach (see Levy

& Markowitz 1979, Kroll et al 1984, and Van Mieghem 2003) Moreover, some meaningful and applicable objectives, such as the safety first objective (Roy 1952), can be formulated under the mean-variance framework Despite all kinds of arguments on the mean-variance approach, it is adopted as the performance measure in this chapter because it’s “applicable, intuitive and implementable” In addition, more analytical results can be generated under this approach On the other hand, even though the mean-variance and utility function approaches are well-established in finance, their applications in supply chain management are not yet fully revealed In fact, most research works on this important topic appear only

in recent years We review some of them as follows

First, in Lau (1980), instead of maximizing the expected profit, the author derives an optimal order quantity which maximizes an objective function of the expected profit and standard deviation of profit for the classic newsvendor problem Next, Eechhoudt et al (1995) study the classic newsvendor problem with risk averse newsvendor via a utility function approach and obtain some interesting findings on the optimal stocking quantity Later on, Lau and Lau (1999) directly extend the work of Pasternack (1985) and study a single-manufacturer single-retailer supply chain model under which both the retailer and manufacturer seek to maximize a linear objective function of the expected profit and variance of profit Choi et al (2008) analyze via a mean-variance approach the supply chains under returns policy in both decentralized and centralized settings Implications for setting returns contracts for achieving channel coordination with risk considerations are discussed Some other recent research works which analyse the risk issues in supply chain management include a qualitative discussion on proactive supply management and its close relationship with risk management (Smeltzer & Siferd 1998), a quantitative analysis of the role of intermediaries in supply chains to reduce financial risk (Agrawal & Seshadri 2000), a mean-variance analysis

of single echelon inventory problems (Chen & Federgruen 2000), a study of the risk-free perishable item returns policy with a risk neutral retailer in a two-echelon supply chain (Webster & Weng 2000), an investigation of the use of capacity options in managing risk

from demand uncertainty (Tan 2002), an analysis of the use of commitment-option for supply chain contract setting with forecast updates (Buzacott et al 2003), a study on contracting scheme with risk preferences considerations (Bassok & Nagarajan 2004), a mean-variance analysis for the newsvendor problem with and without the opportunity cost of stock out (Choi et al 2007a), and a study on channel coordination in supply chains under mean-variance objectives (Choi et al 2007b)

3 Supply chain model

Consider a two-echelon supply chain with one manufacturer and one retailer The retailer sells a fashionable product and faces an uncertain market demand The manufacturer bears

a unit product cost of c and sells the product to the retailer with a unit wholesale price w For the retailer the unit product’s selling price is r At the end of the selling season, there is a salvage market in which any product leftover can be salvaged at a unit price v Let the market demand faced by the retailer be x with a probability density function f(x), and a corresponding cumulative distribution function F(x) We assume that there is a one-to-one mapping between F(·) and its argument We consider the following sequence of action: The

manufacturer will first announce the wholesale price and other parameters (with respect to different kinds of contracts) to the retailer, the retailer will react by placing an order with a

quantity q We assume that the manufacturer can always fulfil the required order quantity

placed by the retailer For a notational purpose, define:

2 0

0

2)(q q³q F x dx ³q xF x dx ³q F x dx

[

Table 1 below gives the profit, expected profit, standard deviation of profit of the simple supply chain described above Observe that the manufacturer is risk free and can always make a positive profit when the wholesale price is larger than the production cost under this simple supply chain

Under the buy-back contract, by the end of the selling season, the retailer can return the

unsold products to the manufacturer for a partial refund with a unit buy-back price b, where

w

b

vd  The returned products have a unit value of v to the manufacturer We can derive

the profit, expected profit, and standard deviation of profit under the buy-back contract for

the supply chain, the retailer, and the manufacturer respectively as shown in Table 2 (see Choi et al 2008 for the details of derivations)

)

( rw q rb³q F x d x

)()( wc q bv³q F x d x

)()(

3.2 Wholesale pricing and profit sharing contract

Under the wholesale pricing and profit sharing contract, the manufacturer controls the

wholesale price w, where w can be set to be c, i.e., the manufacturer is supplying at cost and

makes zero profit from the direct supply On the other hand, the manufacturer will share the retailer’s profit with a proportion of (1D), where 0D1 To be specific, we can derive the following the profit, expected profit and standard deviation of profit under the wholesale pricing and profit sharing contract for the supply chain, the retailer, and the manufacturer, respectively:

P (rc)q(rv)(qx) D[(rw)q(rv)(qx)] (w  q c)  (1 )D ˜

]))(

()[(rw q rv qx

E

P

x d x F v r q

c

r ) (  )³0q ( )

0 F x d x v

r q w

r   ³ qD



 q c

w )( (1 )D ˜

])()()[(

0 F x d x v

r q w

r   ³q

SP (rv) [(q) D(rv) [(q) (1D)(rv) [(q)

Table 3: Profit, Expected Profit, and Standard Deviation of Profit under the Wholesale

Pricing and Profit Sharing Contract

Remarks and findings:

i Please notice that under both buy-back contract and the wholesale pricing and profit sharing contract, the expected profit functions of both the retailer and supply chain are

concave in q, and their standard deviation of profit functions are increasing in q (see

Choi et al 2007a for more details)

ii A direct observation from the expected profit and standard deviation of profit expressions for the manufacturer in Tables 1, 2 and 3 indicates that the manufacturer is basically risk free under the simple supply chain without additional contracts However, under both the buy-back contract and wholesale pricing and profit sharing

contract, the manufacturer needs to bear a higher risk As a result, depending on the degree of risk aversion of the manufacturer, exercising one of these contracts is not always beneficial because the risk level for the manufacturer is higher

iii From Tables 1, 2 and 3, we can see that the sum of retailer’s SP and manufacturer’s SP equals the supply chain’s SP The same applies for the expected profit EP As a result, a change of the contract parameter, of either the buy-back contract and the wholesale pricing and profit sharing contract, can lead to a reallocation of benefit (expected profit) and risk (standard deviation of profit) between the manufacturer and the retailer Bargaining power hence plays a crucial role especially for the wholesale pricing and profit sharing contract

4 Mean-variance decision models

We now consider the above proposed supply chain in which the manufacturer acts as a supply chain coordinator Here, instead of maximizing the supply chain’s expected profit, the manufacturer adopts the following MV objective for the supply chain:

)

1

(P

.)(

)(max

SC SC

SC q

k q SP t s

q EP

d

The objective of (P1) is to maximize the supply chain’s expected profit subject to a constraint

on the supply chain’s standard deviation of profit, where k SC is a positive constant Represent by q SC,EP* F1[(rc)/(rv)]

the product quantity which maximizes EP SC (q)

The efficient frontier for (P1) can be constructed with q[0,q SC ,EP*], and [0,q SC ,EP*] is the

efficient region In (P1), a smaller k SC implies that the manufacturer (who is the decision maker) is more conservative and risk averse We thus call k SC the supply chain’s risk aversion

threshold Notice that when k SC [0,SP SC(q SC ,EP*)], a smaller value of k SC would lead to a

smaller optimal quantity for (P1) because in this region: EP SC (q) is increasing and concave,SP SC (q) is increasing, and the constraint SP SC(q)dk SC is active When

)(max

R R

R q

k q SP t s

q EP

d

In (P2), the retailer tries to maximize his expected profit with the corresponding standard

deviation of profit under control, i.e., SP R(q)dk R, where k R is a positive constant and it is

the retailer’s risk aversion threshold When the manufacturer has specified the details on the

wholesale price and other contract parameters, the retailer will determine an order quantity

*

R

q which optimizes (P2) Observe that there exists a unique q R,MV* (see Choi et al 2007a for the details)

In general, *q and q R,MV* are different In this chapter, we consider the best product quantity

for the supply chain in the mean-variance domain as * q As a consequence, the manufacturer

who acts as the supply chain coordinator can consider using some incentive alignment

schemes to try to entice the retailer to order in a quantity which is equal to *q We will

now explore how the buy-back contract and the wholesale pricing and profit sharing

contract can help to achieve this kind of coordination in a mean-variance domain We

separate the analysis into two parts in the next two sections

5 Coordination by the buy-back contract in the mean-variance domain

Under the presence of the buy-back contract, we rewrite (P2) into (P2(b)) as follows,

;[

]

;[max

R R

R q

k b q SP t s

b q EP

dwhere EP R [ b q; ]=(rw)q(rb)³0q F(x)d x,SP R [ b q; ]=(rb) [(q) (see Table 2), and b is the

buy-back price offered by the manufacturer Denote the optimal order quantity for (P2(b))

by q R,BB*(b) Following the approach in Choi et al (2008), for any given b, we define the

following:

)(

)]

/(

)[(

Notice that q R,*(b) is the order quantity which maximizes the retailer’s expected profit with

a given b The following procedure, Procedure 1, provides the steps to identify the buy-back

price which can achieve coordination (b SC,MV*):

Procedure 1

Step 1 Compute *q by solving (P1).

Step 2 Determine a parameter b* which makes q R,*(b) = *q as follows:

)(

* , b

*)()(rb q k R

*)(/ q k r

œ or b rk R/ [(q*)

Since b ,r b rk R/ [(q*) is rejected:

*

b

? r(k R/ [(q*)) (4)

Step 4. Check for the feasibility of b SC ,MV* b*:

x If SP R(q R,*|b*)dk R, then q R,BB*(b*)= q R,*(b*) Thus, setting b b* would yield

)

( *

*

q R BB =q R,*(b) * Set b SC ,MV* b* and stop

x If SP R(q R,*|b*)!k R, then q R,BB*(b*)=q R, *(b*) However, setting b b* would not

yield q R,BB*(b*) * since setting b b* can only achieve q R,*(b) *, but here

Step 5. Check for the feasibility ofb SC ,MV* b* (after Step 4):

x If SP R(q R,*|b*)!k R, then q R,BB*(b*)=q R,*(b*) Thus, setting b b* would yield

)

(

*

q R BB =q R,*(b) q SC ,MV* Set b SC ,MV* b* and stop

x If SP R(q R,*|b*)dk R, then q R,BB*(b*)= q R,*(b*) In this case, setting b b* can only

achieve q R,*(b) * (but not q R,*(b*) * which implies q R,BB*(b*) *) Thus, we

are not able to achieve q R,BB*(b*) * In this situation, setting both b SC ,MV* b* and

*

,MV

SC

b b* cannot achieve coordination in the MV domain

Procedure 1 gives us the detailed steps for identifying the buy-back price which can achieve

coordination in a mean-variance domain Since the buy-back price is bounded between v

and w, i.e vdbw, a checking on the computed value of b SC ,MV* with respect to this

bound is a required feasibility test

6 Coordination by the wholesale pricing and profit sharing contract in the

;[

],

;[max

R R

R q

k w q SP t s

w q EP

d

DD

where EP R[q;w,D]= [( ) ( ) ( ) ]

0 F x d x v

r q w

r   ³ q

D ,SP R[q;w,D]=D(rv) [(q) (see Table 3),

D is the proportion of profit that the retailer takes and w is wholesale price offered by the

manufacturer to the retailer Represent the optimal quantity which maximizes (P2(w,D)) by

)]

/(

)[(

Notice that q R,*(w) is the order quantity which maximizes the retailer’s expected profit with

a given w and it is independent of D Suppose that D is initially set to be Do(where

1

0Do ) upon the negotiation between the retailer and the manufacturer The following

procedure gives the steps to identify the wholesale price and/or the necessary adjustment in

D in order to achieve coordination in the mean-variance domain:

Procedure 2

Step 1 Compute *q by solving (P1).

Step 2 Determine a parameter *w which makes q R,*(w) = *q as follows:

)(

* , wD

œ SP R(q*|w,D)k R 0

R

k q v

r

œD( ) [( *)

?D*

*)()(r v q

k R

[

Step 4 Check for the feasibility of setting the wholesale price w = * w with D = Do

x If SP R(q R,*(w w*)|Do)dk R , then setting w = w* with D = Do can already make

q R WP * Thus, we can set the wholesale price w = * w with D = Do, and

stop; otherwise, go to Step 5

Step 5. Check for the feasibility of setting another value of D

x If SP R(q R,*(w w*)|Do)!k R, then:

x Option 1: The manufacturer can try to negotiate with the retailer and set a value of

D = D1 (where 0D11) with which SP R(q R,*(w w*)|D1)dk R

x Option 2: The manufacturer can check and see if D*1 If D*1, then the

manufacturer can propose to the retailer by setting a value of D = *D (where

1

*

0  D  ) which can make q R,WP*(w,D) *

Procedure 2 provides to us some guidelines for determining the contract parameters of the

wholesale pricing and profit sharing contract which can help to achieve coordination in the

mean-variance domain

7 Conclusion

In this chapter, we have conducted a mean-variance analysis for supply chains under a back contract and a wholesale pricing and profit sharing contract We characterize in the supply chain the return and the risk by the expected profit and the standard deviation of profit, respectively We focus our discussions on the centralized supply chains From the structural properties of the supply chain, we find that the buy-back price and the wholesale price are simply internal money transfers between the retailer and the manufacturer A change of these prices will lead to a change of the profit and risk sharing between the retailer and the manufacturer We illustrate how a buy-back contract and a wholesale pricing and profit sharing contract can coordinate a supply chain in a mean-variance domain Efficient procedures are proposed The necessary and sufficient conditions for the optimal contract parameters to be found in its feasible region can then be determined Observe that channel coordination in the mean-variance domain is not always achievable This finding is important because when we ignore the risk aversions of the individual supply chain members (as what most papers in the literature assume), channel coordination can always be achieved by setting a buy-back contract and a wholesale pricing and profit sharing contract However, in the real-world, different supply chain members have different degrees of risk aversion, and hence a realistic contract should be set with respect to the risk aversions of these individual decision makers Moreover, intuitively, when the risk aversions between the supply chain coordinator and the retailer are too far away, channel coordination may not be achievable and this point can be revealed by using our analytical models From the studies in this chapter, we can see that the mean-variance model can provide a systematic framework for studying channel coordination issues in stochastic supply chain models with risk and profit considerations This framework can be further extended and used to study a large variety of supply chain contracts

buy-8 References

Choi, T.M., Li, D & Yan, H (2007a) Mean-Variance Analysis of Newsvendor Problem To

appear in IEEE Transactions on Systems, Man, and Cybernetics: Part A

Choi, T.M., Li, D & Yan, H (2008) Mean-variance analysis of a single supplier and retailer

supply chain under a returns policy European Journal of Operational Research, 184,

356-376

Choi, T.M., Li, D., Yan, H & Chiu, C.H (2007b) Channel coordination in supply chains with

agents having mean-variance objectives Forthcoming in Omega, available online in

ScienceDirect.com, doi : 10.1016/j.omega.2006.12.003

Agrawal, V & Seshadri, S (2000) Risk intermediation in supply chains IIE Transactions, 32,

819-831

Bassok, Y & Nagarajan, M (2004) Contracting under risk preferences Working paper,

University of Southern California

Buzacott, J., Yan, H & Zhang, H (2003) Risk analysis of commitment-option contracts with

forecast updates Working paper, York University

Cachon, GP (2003) Supply chain coordination with contracts Working paper, University of

Pennsylvania,

Chen, F & Federgruen, A (2000) Mean-variance analysis of basic inventory models

Working paper, Columbia University

Eeckhoudt, L., Gollier, C & Schlesinger, H (1995) The risk averse (prudent) newsboy

Management Science, 41, 786-794

Kroll, Y., Levy, H & Markowitz, H.M (1984) Mean-variance versus direct utility

maximization,” Journal of Finance, 39, 47-61

Lau, H.S (1980) The newsboy problem under alternative optimization objectives Journal of

the Operational Research Society, 31, 525-535

Lau, H.S & Lau, A.H.L (1999) Manufacturer's pricing strategy and returns policy for a

single-period commodity European Journal of Operational Research, 116, 291-304

Levy, H & Markowitz, H.M (1979) Approximated expected utility by a function of mean

and variance American Economics Review, 69, 308-317

Luenberger, DG (1998) Investment Science Oxford University Press

Markowitz, H.M (1959) Portfolio Selection: Efficient Diversification of Investment New York:

John Wiley & Sons

Pasternack, B.A (1985) Optimal pricing and returns policies for perishable commodities

Marketing Science, 4, 166-176

Roy, A.D (1952) Safety first and the holding of assets Econometrica, 20, 431-449

Smeltzer, L.R & Siferd, S.P (1998) Proactive supply management: The management of risk

International Journal of Purchasing & Materials Management, Winter, 38-45

Tan, B (2002) Managing manufacturing risks by using capacity options Journal of the

Operational Research Society, 53, 232-242

Tsay, A.A., Nahmias, S & Agrawal, N (1999) Modelling supply chain contracts: a review

In: Quantitative Models for Supply Chain Management, Tayur S et al (Eds), Kluwer

Academic Publishers, 299-336

Van Mieghem, J.A (2003) Capacity management, investment, and hedging: Review and

recent developments Manufacturing and Service Operations Management, 5, 269-301

Webster, S & Weng, Z.K (2000) A risk-free perishable item returns policy Manufacturing

and Service Operations Management, 2, 100-106

9 Acknowledgements

This work is partially supported by the RGC Competitive Earmarked Research Grant PolyU5146/05E, and the internal fundings provided by the Hong Kong Polytechnic University The author would like to dedicate this piece of work to Bryan Choi

Developing Supply Chain Management System

Evaluation Attributes Based on the

Supply Chain Strategy

1Ching Yun University,

2National Pingtung Institute of Commerce,

Taiwan

1 Introduction

Given constantly fluctuating market demands, short life cycles of products and global market trends, companies must effectively design, produce and deliver products and services (Christopher & Juttner, 2000) A Supply Chain Management (SCM) system involves managing and coordinating all activities associated with goods and information flows from those raw materials sourcing to product delivery and, finally, to the end customers A SCM system incorporates numerous modules of supply chain planning and execution, e.g., supply chain network configuration, demand planning, manufacturing planning and scheduling, distribution planning, transportation management, inventory and warehouse management, and supply chain event management, etc This is why more companies are seeing SCM systems as the key to enhance the transparency, sharing, and trust of their supply chains

Min & Zhou (2002) postulated that information technology (IT) provides the impetus for supply chain cooperation and re-engineering Here, a SCM system is defined as an integrated enterprise information system (EIS) to realize the integration and collaboration of different stages within a supply chain and owns analytical capabilities to produce planning solutions, strategic level decisions and executing tasks of supply chain A lot of companies invest large money and efforts in SCM applications to increase their competitive advantages and improve overall supply chain efficiency As a SCM system becomes more organizationally encompassing, so that its selection is complicated in nature rather than just traditional information system (IS) selection (Sarkis & Sundarraj, 2000) However, many companies install their SCM systems hurriedly without fully understanding the implications for their business or the need for compatibility with overall organizational goals and strategies The result of this hasty approach is failed projects or weak systems whose logics conflict with organizational goals However, the impact of bad decision can be high not only in system operations but in terms of its impact on management attitude

Davenport (1998) emphasized the technical factors are not the main reason EIS fail, however, the biggest problems are business problems The performance of a SCM system basically relates to the degree of match between the available system functionalities and the company’s requirements and also between the logic assumed in the system and that of the